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          <h1 class="post-title" itemprop="name headline">Java编写MapReduce之Partitioner</h1>
        

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        <p>MapReduce编程之Partitioner<br><a id="more"></a></p>
<h2 id="Partitioner介绍"><a href="#Partitioner介绍" class="headerlink" title="Partitioner介绍"></a>Partitioner介绍</h2><p>决定MapTask的结果放入哪个ReduceTask执行。因此，我们要将工作均匀的分配给ReduceTask执行。</p>
<h2 id="案例"><a href="#案例" class="headerlink" title="案例"></a>案例</h2><p>我们有这样一个input:<br><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line">xiaomi	400</span><br><span class="line">huawei	800</span><br><span class="line">xiaomi	330</span><br><span class="line">iphone	500</span><br><span class="line">iphone	400</span><br><span class="line">huawei	600</span><br><span class="line">vivo	400</span><br></pre></td></tr></table></figure></p>
<p>要统计xiaomi、huawei、iphone以及其他的手机销售数目总数。我们可以借助Partitioner将xiaomi、huawei、iphone以及其他的手机分别放入不同的ReduceTask执行。</p>
<h2 id="Java程序"><a href="#Java程序" class="headerlink" title="Java程序"></a>Java程序</h2><figure class="highlight java"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br><span class="line">86</span><br><span class="line">87</span><br><span class="line">88</span><br><span class="line">89</span><br><span class="line">90</span><br><span class="line">91</span><br><span class="line">92</span><br><span class="line">93</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">public</span> <span class="class"><span class="keyword">class</span> <span class="title">PartitionerApp</span> <span class="keyword">extends</span> <span class="title">Configured</span> <span class="keyword">implements</span> <span class="title">Tool</span> </span>&#123;</span><br><span class="line"></span><br><span class="line">    <span class="keyword">private</span> <span class="keyword">static</span> <span class="keyword">final</span> Logger LOG = LoggerFactory.getLogger(PartitionerApp.class);</span><br><span class="line"></span><br><span class="line">    <span class="meta">@Override</span></span><br><span class="line">    <span class="function"><span class="keyword">public</span> <span class="keyword">int</span> <span class="title">run</span><span class="params">(String[] args)</span> <span class="keyword">throws</span> Exception </span>&#123;</span><br><span class="line">        <span class="comment">// 创建Configuration</span></span><br><span class="line">        Configuration conf = <span class="keyword">new</span> Configuration();</span><br><span class="line"></span><br><span class="line">        <span class="comment">// 如果删除路径存在，则删除</span></span><br><span class="line">        FileSystem fs = FileSystem.get(URI.create(<span class="string">"hdfs://111.231.238.23:9000"</span>), conf);</span><br><span class="line">        LOG.warn(<span class="string">"输出路径&#123;&#125;如果存在，则会被自动删除！！！"</span>, args[<span class="number">1</span>]);</span><br><span class="line">        fs.deleteOnExit(<span class="keyword">new</span> Path(args[<span class="number">1</span>]));</span><br><span class="line"></span><br><span class="line">        <span class="comment">// 创建Job</span></span><br><span class="line">        Job job = Job.getInstance(conf, <span class="string">"partitionerApp"</span>);</span><br><span class="line"></span><br><span class="line">        <span class="comment">// 设置job的处理类</span></span><br><span class="line">        job.setJarByClass(WordCount.class);</span><br><span class="line"></span><br><span class="line">        <span class="comment">// 设置作业的输入路径</span></span><br><span class="line">        FileInputFormat.addInputPath(job, <span class="keyword">new</span> Path(args[<span class="number">0</span>]));</span><br><span class="line"></span><br><span class="line">        <span class="comment">// 设置Map相关的参数（主类、输出的key/value类型）</span></span><br><span class="line">        job.setMapperClass(Map.class);</span><br><span class="line">        job.setMapOutputKeyClass(Text.class);</span><br><span class="line">        job.setMapOutputValueClass(LongWritable.class);</span><br><span class="line"></span><br><span class="line">        <span class="comment">// 设置Reduce相关参数（主类、输出的key/value类型）</span></span><br><span class="line">        job.setReducerClass(Reduce.class);</span><br><span class="line">        job.setOutputKeyClass(Text.class);</span><br><span class="line">        job.setOutputValueClass(LongWritable.class);</span><br><span class="line"></span><br><span class="line">        <span class="comment">// 设置job的Partitioner处理类</span></span><br><span class="line">        job.setPartitionerClass(Partition.class);</span><br><span class="line">        <span class="comment">// 指定ReduceTask的个数，与自己Partitioner处理类的数目要一致</span></span><br><span class="line">        job.setNumReduceTasks(<span class="number">4</span>);</span><br><span class="line"></span><br><span class="line">        <span class="comment">// 设置作业的输出路径</span></span><br><span class="line">        FileOutputFormat.setOutputPath(job, <span class="keyword">new</span> Path(args[<span class="number">1</span>]));</span><br><span class="line"></span><br><span class="line">        <span class="comment">// 提交作业，等待完成</span></span><br><span class="line">        <span class="keyword">return</span> job.waitForCompletion(<span class="keyword">true</span>) ? <span class="number">0</span> : <span class="number">1</span>;</span><br><span class="line">    &#125;</span><br><span class="line"></span><br><span class="line">    <span class="function"><span class="keyword">public</span> <span class="keyword">static</span> <span class="keyword">void</span> <span class="title">main</span><span class="params">(String[] args)</span> <span class="keyword">throws</span> Exception </span>&#123;</span><br><span class="line">        System.exit(ToolRunner.run(<span class="keyword">new</span> PartitionerApp(), args));</span><br><span class="line">    &#125;</span><br><span class="line"></span><br><span class="line">    <span class="keyword">public</span> <span class="keyword">static</span> <span class="class"><span class="keyword">class</span> <span class="title">Map</span> <span class="keyword">extends</span> <span class="title">Mapper</span>&lt;<span class="title">LongWritable</span>, <span class="title">Text</span>, <span class="title">Text</span>, <span class="title">LongWritable</span>&gt; </span>&#123;</span><br><span class="line"></span><br><span class="line">        <span class="meta">@Override</span></span><br><span class="line">        <span class="function"><span class="keyword">public</span> <span class="keyword">void</span> <span class="title">map</span><span class="params">(LongWritable key, Text value, Context context)</span> <span class="keyword">throws</span> IOException, InterruptedException </span>&#123;</span><br><span class="line">            <span class="comment">// 一行数据</span></span><br><span class="line">            String line = value.toString();</span><br><span class="line">            String[] split = line.split(<span class="string">"\t"</span>);</span><br><span class="line">            context.write(<span class="keyword">new</span> Text(split[<span class="number">0</span>]), <span class="keyword">new</span> LongWritable(Long.parseLong(split[<span class="number">1</span>])));</span><br><span class="line">        &#125;</span><br><span class="line">    &#125;</span><br><span class="line"></span><br><span class="line">    <span class="keyword">public</span> <span class="keyword">static</span> <span class="class"><span class="keyword">class</span> <span class="title">Partition</span> <span class="keyword">extends</span> <span class="title">Partitioner</span>&lt;<span class="title">Text</span>, <span class="title">LongWritable</span>&gt; </span>&#123;</span><br><span class="line"></span><br><span class="line">        <span class="meta">@Override</span></span><br><span class="line">        <span class="function"><span class="keyword">public</span> <span class="keyword">int</span> <span class="title">getPartition</span><span class="params">(Text key, LongWritable value, <span class="keyword">int</span> numPartitions)</span> </span>&#123;</span><br><span class="line">            <span class="keyword">switch</span> (key.toString()) &#123;</span><br><span class="line">                <span class="keyword">case</span> <span class="string">"xiaomi"</span>:</span><br><span class="line">                    <span class="keyword">return</span> <span class="number">0</span>;</span><br><span class="line">                <span class="keyword">case</span> <span class="string">"huawei"</span>:</span><br><span class="line">                    <span class="keyword">return</span> <span class="number">1</span>;</span><br><span class="line">                <span class="keyword">case</span> <span class="string">"iphone"</span>:</span><br><span class="line">                    <span class="keyword">return</span> <span class="number">2</span>;</span><br><span class="line">                <span class="keyword">default</span>:</span><br><span class="line">                    <span class="keyword">return</span> <span class="number">3</span>;</span><br><span class="line">            &#125;</span><br><span class="line">        &#125;</span><br><span class="line">    &#125;</span><br><span class="line"></span><br><span class="line">    <span class="keyword">public</span> <span class="keyword">static</span> <span class="class"><span class="keyword">class</span> <span class="title">Reduce</span> <span class="keyword">extends</span> <span class="title">Reducer</span>&lt;<span class="title">Text</span>, <span class="title">LongWritable</span>, <span class="title">Text</span>, <span class="title">LongWritable</span>&gt; </span>&#123;</span><br><span class="line"></span><br><span class="line">        <span class="meta">@Override</span></span><br><span class="line">        <span class="function"><span class="keyword">public</span> <span class="keyword">void</span> <span class="title">reduce</span><span class="params">(Text key, Iterable&lt;LongWritable&gt; values, Context context)</span></span></span><br><span class="line"><span class="function">                <span class="keyword">throws</span> IOException, InterruptedException </span>&#123;</span><br><span class="line">            <span class="keyword">long</span> sum = <span class="number">0</span>;</span><br><span class="line">            <span class="keyword">for</span> (LongWritable val : values) &#123;</span><br><span class="line">                <span class="comment">// 累计数目</span></span><br><span class="line">                sum += val.get();</span><br><span class="line">            &#125;</span><br><span class="line">            <span class="comment">// 通过上下文将结果输出</span></span><br><span class="line">            context.write(key, <span class="keyword">new</span> LongWritable(sum));</span><br><span class="line">        &#125;</span><br><span class="line">    &#125;</span><br><span class="line"></span><br><span class="line">&#125;</span><br></pre></td></tr></table></figure>
<p>我们要注意的这两行代码：<br><figure class="highlight java"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">// 设置job的Partitioner处理类</span></span><br><span class="line">job.setPartitionerClass(Partition.class);</span><br><span class="line"><span class="comment">// 指定ReduceTask的个数，与自己Partitioner处理类的数目要一致</span></span><br><span class="line">job.setNumReduceTasks(<span class="number">4</span>);</span><br></pre></td></tr></table></figure></p>
<h2 id="打包-amp-上传-amp-运行"><a href="#打包-amp-上传-amp-运行" class="headerlink" title="打包&amp;上传&amp;运行"></a>打包&amp;上传&amp;运行</h2><p>略</p>
<h2 id="代码"><a href="#代码" class="headerlink" title="代码"></a>代码</h2><p><a href="https://github.com/xuanbo/hadoop-examples/tree/master/map-reduce-example" target="_blank" rel="noopener">戳我</a></p>

      
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